Comparer des méthodes

	Autoencodeur variationnel faiblement supervisé	Apprentissage semi-supervisé
Domaine	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2014–2018	1970s–2006 (formalized)
Auteur d'origine	Kingma, D. P. et al. (building on VAE and semi-supervised deep generative models)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Generative model with weak supervision	Learning paradigm
Source fondatrice	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR 2014).	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	WS-VAE, weakly-supervised VAE, semi-supervised VAE with weak labels, label-guided variational autoencoder	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	3	5
Résumé	A Weakly Supervised Variational Autoencoder (WS-VAE) extends the standard VAE generative framework by incorporating partial, noisy, or coarse supervision signals — such as crowd-sourced labels, heuristic rules, or programmatic annotations — to guide latent space learning without requiring fully annotated data. It is widely applied in computer vision, NLP, and biomedical domains where complete ground-truth labels are expensive or unavailable.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.